
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




class 					lowerCamelCase__				(							__lowercase	,		__lowercase	,		__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	StableDiffusionControlNetImgaImgPipeline
	_A								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	_A								=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_A								=	IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'})
	_A								=	IMAGE_TO_IMAGE_IMAGE_PARAMS







	def   _lowerCamelCase							(   self						:Any		)						->					Union[str, Any]:
								torch.manual_seed(0		)
								__UpperCamelCase      :  int       =   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  Optional[Any]       =   ControlNetModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  Tuple       =   DDIMScheduler(
								    beta_start=0.00085	,  beta_end=0.012	,  beta_schedule="scaled_linear"	,  clip_sample=a	,  set_alpha_to_one=a	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  int       =   AutoencoderKL(
								    block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  Optional[int]       =   CLIPTextConfig(
								    bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1E-0_5	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
								__UpperCamelCase      :  List[str]       =   CLIPTextModel(a		)
								__UpperCamelCase      :  int       =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								__UpperCamelCase      :  Optional[int]       =   {
								    "unet": unet,
								    "controlnet": controlnet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components







	def   _lowerCamelCase							(   self						:List[str]	,  a						:Dict	,  a						:List[str]=0		)						->					int:
								if str(a		).startswith("mps"		):
															__UpperCamelCase      :  int       =   torch.manual_seed(a		)
								else:
															__UpperCamelCase      :  Tuple       =   torch.Generator(device=a		).manual_seed(a		)

								__UpperCamelCase      :  Optional[int]       =   2
								__UpperCamelCase      :  Tuple       =   randn_tensor(
								    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a		)	,  )
								__UpperCamelCase      :  Optional[Any]       =   floats_tensor(control_image.shape	,  rng=random.Random(a		)		).to(a		)
								__UpperCamelCase      :  Optional[Any]       =   image.cpu().permute(0	,  2	,  3	,  1		)[0]
								__UpperCamelCase      :  int       =   Image.fromarray(np.uinta(a		)		).convert("RGB"		).resize((6_4, 6_4)		)
								__UpperCamelCase      :  Union[str, Any]       =   {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								    "image": image,
								    "control_image": control_image,
								}

								return inputs







	def   _lowerCamelCase							(   self						:List[str]		)						->					Union[str, Any]:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)







	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()	,  reason="XFormers attention is only available with CUDA and `xformers` installed"	,  )
	def   _lowerCamelCase							(   self						:str		)						->					int:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					int:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)




class 					lowerCamelCase__				(							__lowercase	,		__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	StableDiffusionControlNetImgaImgPipeline
	_A								=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
	_A								=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	_A								=	frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







	def   _lowerCamelCase							(   self						:Tuple		)						->					Optional[Any]:
								torch.manual_seed(0		)
								__UpperCamelCase      :  Union[str, Any]       =   UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  sample_size=3_2	,  in_channels=4	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=3_2	,  )
								torch.manual_seed(0		)

								def init_weights(a						:Optional[int]		):
															if isinstance(a	,  torch.nn.Convad		):
																						torch.nn.init.normal(m.weight		)
																						m.bias.data.fill_(1.0		)

								__UpperCamelCase      :  Union[str, Any]       =   ControlNetModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
								controlneta.controlnet_down_blocks.apply(a		)

								torch.manual_seed(0		)
								__UpperCamelCase      :  Tuple       =   ControlNetModel(
								    block_out_channels=(3_2, 6_4)	,  layers_per_block=2	,  in_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  cross_attention_dim=3_2	,  conditioning_embedding_out_channels=(1_6, 3_2)	,  )
								controlneta.controlnet_down_blocks.apply(a		)

								torch.manual_seed(0		)
								__UpperCamelCase      :  List[str]       =   DDIMScheduler(
								    beta_start=0.00085	,  beta_end=0.012	,  beta_schedule="scaled_linear"	,  clip_sample=a	,  set_alpha_to_one=a	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  Any       =   AutoencoderKL(
								    block_out_channels=[3_2, 6_4]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  )
								torch.manual_seed(0		)
								__UpperCamelCase      :  Optional[Any]       =   CLIPTextConfig(
								    bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1E-0_5	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
								__UpperCamelCase      :  int       =   CLIPTextModel(a		)
								__UpperCamelCase      :  Any       =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								__UpperCamelCase      :  Tuple       =   MultiControlNetModel([controlneta, controlneta]		)

								__UpperCamelCase      :  List[Any]       =   {
								    "unet": unet,
								    "controlnet": controlnet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								return components







	def   _lowerCamelCase							(   self						:str	,  a						:List[str]	,  a						:int=0		)						->					Optional[Any]:
								if str(a		).startswith("mps"		):
															__UpperCamelCase      :  Optional[int]       =   torch.manual_seed(a		)
								else:
															__UpperCamelCase      :  Optional[Any]       =   torch.Generator(device=a		).manual_seed(a		)

								__UpperCamelCase      :  Any       =   2

								__UpperCamelCase      :  List[str]       =   [
								    randn_tensor(
								        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a		)	,  ),
								    randn_tensor(
								        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,  generator=a	,  device=torch.device(a		)	,  ),
								]

								__UpperCamelCase      :  Optional[Any]       =   floats_tensor(control_image[0].shape	,  rng=random.Random(a		)		).to(a		)
								__UpperCamelCase      :  Tuple       =   image.cpu().permute(0	,  2	,  3	,  1		)[0]
								__UpperCamelCase      :  Any       =   Image.fromarray(np.uinta(a		)		).convert("RGB"		).resize((6_4, 6_4)		)
								__UpperCamelCase      :  int       =   {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								    "image": image,
								    "control_image": control_image,
								}

								return inputs







	def   _lowerCamelCase							(   self						:int		)						->					Tuple:
								__UpperCamelCase      :  List[str]       =   self.get_dummy_components()
								__UpperCamelCase      :  Dict       =   self.pipeline_class(**a		)
								pipe.to(a		)

								__UpperCamelCase      :  int       =   10.0
								__UpperCamelCase      :  Tuple       =   4

								__UpperCamelCase      :  str       =   self.get_dummy_inputs(a		)
								__UpperCamelCase      :  List[str]       =   steps
								__UpperCamelCase      :  Optional[int]       =   scale
								__UpperCamelCase      :  Dict       =   pipe(**a		)[0]

								__UpperCamelCase      :  Dict       =   self.get_dummy_inputs(a		)
								__UpperCamelCase      :  List[Any]       =   steps
								__UpperCamelCase      :  Union[str, Any]       =   scale
								__UpperCamelCase      :  List[str]       =   pipe(**a	,  control_guidance_start=0.1	,  control_guidance_end=0.2		)[0]

								__UpperCamelCase      :  List[str]       =   self.get_dummy_inputs(a		)
								__UpperCamelCase      :  Tuple       =   steps
								__UpperCamelCase      :  Tuple       =   scale
								__UpperCamelCase      :  Any       =   pipe(**a	,  control_guidance_start=[0.1, 0.3]	,  control_guidance_end=[0.2, 0.7]		)[0]

								__UpperCamelCase      :  Union[str, Any]       =   self.get_dummy_inputs(a		)
								__UpperCamelCase      :  Dict       =   steps
								__UpperCamelCase      :  int       =   scale
								__UpperCamelCase      :  Union[str, Any]       =   pipe(**a	,  control_guidance_start=0.4	,  control_guidance_end=[0.5, 0.8]		)[0]

								# make sure that all outputs are different
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
								assert np.sum(np.abs(output_a - output_a		)		) > 1E-3







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					int:
								return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)







	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()	,  reason="XFormers attention is only available with CUDA and `xformers` installed"	,  )
	def   _lowerCamelCase							(   self						:List[Any]		)						->					str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







	def   _lowerCamelCase							(   self						:Tuple		)						->					List[str]:
								self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







	def   _lowerCamelCase							(   self						:Tuple		)						->					Union[str, Any]:
								__UpperCamelCase      :  Dict       =   self.get_dummy_components()
								__UpperCamelCase      :  str       =   self.pipeline_class(**a		)
								pipe.to(a		)
								pipe.set_progress_bar_config(disable=a		)
								with tempfile.TemporaryDirectory() as tmpdir:
															try:
																						# save_pretrained is not implemented for Multi-ControlNet
																						pipe.save_pretrained(a		)
															except NotImplementedError:
																						pass




@slow
@require_torch_gpu
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def   _lowerCamelCase							(   self						:List[str]		)						->					Tuple:
								__UpperCamelCase      :  Tuple       =   ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"		)

								__UpperCamelCase      :  List[str]       =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
								    "runwayml/stable-diffusion-v1-5"	,  safety_checker=a	,  controlnet=a		)
								pipe.enable_model_cpu_offload()
								pipe.set_progress_bar_config(disable=a		)

								__UpperCamelCase      :  int       =   torch.Generator(device="cpu"		).manual_seed(0		)
								__UpperCamelCase      :  List[str]       =   "evil space-punk bird"
								__UpperCamelCase      :  Union[str, Any]       =   load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		).resize((5_1_2, 5_1_2)		)
								__UpperCamelCase      :  Dict       =   load_image(
								    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"		).resize((5_1_2, 5_1_2)		)

								__UpperCamelCase      :  Optional[int]       =   pipe(
								    a	,  a	,  control_image=a	,  generator=a	,  output_type="np"	,  num_inference_steps=5_0	,  strength=0.6	,  )

								__UpperCamelCase      :  Union[str, Any]       =   output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								__UpperCamelCase      :  Optional[Any]       =   load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"		)

								assert np.abs(expected_image - image		).max() < 9E-2

def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int = 10**9)   ->       int:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   1
							__UpperCamelCase      :  Any       =   2

							__UpperCamelCase      :  Dict       =   0
							__UpperCamelCase      :  Any       =   0
							__UpperCamelCase      :  List[Any]       =   0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														__UpperCamelCase      :  Optional[int]       =   2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
		print(f"{solution() = }")